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DOE OSTI2018

Excitations of the Nucleon – N* spectroscopy with CLAS

Baryon spectroscopy began in 1952 with the discovery of the first Δ resonance by Fermi and collaborators. By the mid-1980s a sizeable collection of resonances had been identified in pion-Nucleon scattering, and particle physics left this field for higher-energy pastures, confident that a basic understanding of the Nstar spectrum was at least close, if not exactly in hand. With time, refinements of the quark model provided a systematic ordering to the observations but, annoyingly, predicted the existence of many more states than had been observed, which lead to speculations of mechanisms that restricted the degrees of freedom. Only recently (2011) have theoretical advances finally allowed a direct computation of the nucleon spectrum in Lattice-QCD, with the startling confirmation of large numbers of "missing" excited states. With the advent of new facilities, the last decade has seen a renaissance of sorts in photo-meson production experiments, in which polarization has been used to constrain the production amplitudes to a degree not achieved in π+N reactions, and a great many new Nstar candidates are emerging. The additional flexibility in the 4-momentum transfer has been used with electron scattering to probe the excitation process and the role of the meson cloud. Highlights in the context of the CLAS N* spectroscopy program at Jefferson Lab will be reviewed.

Sandorfi, Andrew↗
DOE OSTI2014

Young galaxy candidates in the Hubble Frontier Fields. I. A2744

We report the discovery of 24 Lyman-break candidates at 7 ≲ z ≲ 10.5, in the Hubble Frontier Fields (HFF) imaging data of A2744 (z = 0.308), plus Spitzer/IRAC data and archival ACS data. The sample includes a triple image system with a photometric redshift of z ≅ 7.4. This high redshift is geometrically confirmed by our lens model corresponding to deflection angles that are 12% larger than the lower-redshift systems used to calibrate the lens model at z = 2.019. The majority of our high-redshift candidates are not expected to be multiply lensed given their locations in the image plane and the brightness of foreground galaxies, but are magnified by factors of ∼1.3-15, so that we are seeing further down the luminosity function than comparable deep-field imaging. It is apparent that the redshift distribution of these sources does not smoothly extend over the full redshift range accessible at z < 12, but appears to break above z = 9. Nine candidates are clustered within a small region of 20'' across, representing a potentially unprecedented concentration. Given the poor statistics, however, we must await similar constraints from the additional HFF clusters to properly examine this trend. The physical properties of our candidates are examined using the range of lens models developed for the HFF program by various groups including our own, for a better estimate of underlying systematics. Our spectral-energy-distribution fits for the brightest objects suggest stellar masses of ≅ 10{sup 9} M {sub ☉}, star formation rates of ≅ 4 M {sub ☉} yr{sup –1}, and a typical formation redshift of z ≲ 19.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Divalent Ion Parameterization Strongly Affects Conformation and Interactions of an Anionic Biomimetic Polymer

The description of peptides and the use of molecular dynamics simulations to refine structures and investigate the dynamics on an atomistic scale are well developed. Through a consensus in this community over multiple decades, parameters were developed for molecular interactions that only require the sequence of amino-acids and an initial guess for the three-dimensional structure. The recent discovery of peptoids will require a retooling of the currently available interaction potentials in order to have the same level of confidence in the predicted structures and pathways as there is presently in the peptide counterparts. Here we present modeling of peptoids using a combination of ab initio molecular dynamics (AIMD) and atomistic resolution classical forcefield (FF) to span the relevant time and length scales. To properly account for the dominant forces that stabilize ordered structures of peptoids, namely steric-, electrostatic, and hydrophobic interactions mediated through sidechain-sidechain interactions in the FF model, those have to be first mapped out using high fidelity atomistic representations. A key feature here is not only to use gas phase quantum chemistry tools, but also account for solvation effects in the condensed phase through AIMD. One major challenge is to elucidate ion binding to charged or polar regions of the peptoid and its concomitant role in the creation of local order. Here, similar to proteins, a specific ion effect is observed suggesting that both the net charge and the precise chemical nature of the ion will need to be described. MDD was supported by MS3 (Materials Synthesis and Simulation Across Scales) Initiative at Pacific Northwest National Laboratory. Research was funded by the Laboratory Directed Research and Development program at Pacific Northwest National Laboratory. MDB acknowledges support from US Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Material & Engineering. CJM acknowledges support from US Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences & Biosciences. PNNL is a multiprogram national laboratory operated by Battelle for the U.S. Department of Energy.

Daily, Michael D.↗
DOE OSTI2024

Novel Signatures Beyond the Standard Model: From Extreme Environments to Precision Measurements

Most of our universe is dark. It is dark literally—visible matter makes up only 5% of the energy density—and conceptually, as many outstanding questions remain unsolved. The discovery of particle dark matter would shed light on our dark universe and throw open a window on the highest energy scales. This research made progress toward dark matter and new particle searches in three ways. Extreme compact objects, such as black holes, neutron stars, and stellar cores, can parametrically increase the production rates of feebly interacting particles and yield observable signatures. Specifically, a process known as superradiance leads to exponential growth of the number of ultralight particles gravitationally bound to rotating black holes, and turns astrophysical black holes into particle detectors. This research broadened the scope of applicability of superradiance studies to particles with a wide range of interactions and masses, predicting multi messenger signals. When ultralight particles are produced as dark matter in the early universe, they can be detected directly in laboratory experiments. However, these feebly interacting particles lead to small signals that are inevitably difficult to tease out from backgrounds; their detection requires the theoretical development of unique signatures that can be searched for in low-noise experiments. Some dark matter particles could be detectable with thin film metamaterials combined with low noise single-photon detectors. This research develop the theoretical framework and supported experimental efforts for ambitious searches for a wider class of ultralight particles. Overall, the outcome of this theoretical particle physics program developed novel signatures of new dark matter particles, and thereby expanded the particle parameter space which can be explored with astrophysical observations and precision experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Testing light dark matter coannihilation with fixed-target experiments

In this paper, we introduce a novel program of fixed-target searches for thermal-origin Dark Matter (DM), which couples inelastically to the Standard Model. Since the DM only interacts by transitioning to a heavier state, freeze-out proceeds via coannihilation and the unstable heavier state is depleted at later times. For sufficiently large mass splittings, direct detection is kinematically forbidden and indirect detection is impossible, so this scenario can only be tested with accelerators. Here we propose new searches at proton and electron beam fixed-target experiments to probe sub-GeV coannihilation, exploiting the distinctive signals of up- and down-scattering as well as decay of the excited state inside the detector volume. We focus on a representative model in which DM is a pseudo-Dirac fermion coupled to a hidden gauge field (dark photon), which kinetically mixes with the visible photon. We define theoretical targets in this framework and determine the existing bounds by reanalyzing results from previous experiments. We find that LSND, E137, and BaBar data already place strong constraints on the parameter space consistent with a thermal freeze-out origin, and that future searches at Belle II and MiniBooNE, as well as recently-proposed fixed-target experiments such as LDMX and BDX, can cover nearly all remaining gaps. We also briefly comment on the discovery potential for proposed beam dump and neutrino experiments which operate at much higher beam energies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

AUTOMATED TRANSIENT IDENTIFICATION IN THE DARK ENERGY SURVEY

We describe an algorithm for identifying point-source transients and moving objects on reference-subtracted optical images containing artifacts of processing and instrumentation. The algorithm makes use of the supervised machine learning technique known as Random Forest. We present results from its use in the Dark Energy Survey Supernova program (DES-SN), where it was trained using a sample of 898,963 signal and background events generated by the transient detection pipeline. After reprocessing the data collected during the first DES-SN observing season (2013 September through 2014 February) using the algorithm, the number of transient candidates eligible for human scanning decreased by a factor of 13.4, while only 1.0% of the artificial Type Ia supernovae (SNe) injected into search images to monitor survey efficiency were lost, most of which were very faint events. Here we characterize the algorithm’s performance in detail, and we discuss how it can inform pipeline design decisions for future time-domain imaging surveys, such as the Large Synoptic Survey Telescope and the Zwicky Transient Facility. An implementation of the algorithm and the training data used in this paper are available at at http://portal.nersc.gov/project/dessn/autoscan.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Intensity Frontier Instrumentation

The fundamental origin of flavor in the Standard Model (SM) remains a mystery. Despite the roughly eighty years since Rabi asked “Who ordered that?” upon learning of the discovery of the muon, we have not understood the reason that there are three generations or, more recently, why the quark and neutrino mixing matrices and masses are so different. The solution to the flavor problem would give profound insights into physics beyond the Standard Model (BSM) and tell us about the couplings and the mass scale at which the next level of insight can be found. The SM fails to explain all observed phenomena: new interactions and yet unseen particles must exist. They may manifest themselves by causing SM reactions to differ from often very precise predictions. The Intensity Frontier (1) explores these fundamental questions by searching for new physics in extremely rare processes or those forbidden in the SM. This often requires massive and/or extremely finely tuned detectors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Deciphering and Manipulating Low Dimensional Magnetism

This program investigates the electronic structure and collective quantum phenomena of correlated magnetic materials using advanced angle-resolved photoemission spectroscopy (ARPES) and complementary probes, with a focus on three interrelated material families: (i) the semiconducting van der Waals magnet Cr₂Ge₂Te₆, (ii) metallic Fe-based van der Waals magnets including Fe₃GeTe₂ Fe₃GaTe₂, and Fe5GeTe₂, and (iii) kagome magnets such as FeGe, FeSn, and CsV₃Sb₅/CsCr₃Sb₅. In Cr₂Ge₂Te₆, our work established how spin excitations develop across a dimensional crossover as spins establish correlation to form long range order, providing a clean platform to isolate correlation effects. In metallic Fe-based magnets, we uncovered the dichotomy between flat and dispersive bands, revealed momentum-dependent electronic reconstructions tied to magnetic order, and demonstrated reversible, non-volatile electronic switching near room temperature, highlighting the strong coupling among spin, charge, and lattice degrees of freedom in metallic ferromagnets. In kagome magnets, we identified charge density wave formation, symmetry breaking, flat-band renormalization, and field-induced momentum-dependent electronic anisotropy, elucidating how geometric frustration and electronic correlations conspire to generate emergent quantum states. Collectively, these results establish a unified microscopic framework for understanding correlation-driven electronic reconstruction, symmetry breaking, and collective order across semiconducting and metallic magnetic systems, advancing DOE mission goals in quantum materials discovery and control.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Brochure for the DOE Office of Science Workshop on Envisioning Frontiers in AI and Computing for Biological Research

In February of 2025 a joint ASCR/BER workshop was held to identify key transformational research directions for understanding biology using artificial intelligence (AI), digital twins and high-performance (HPC) computational methods to facilitate scientific discovery and innovation in support of the Department of Energy mission. AI technologies offer exciting new groundbreaking methods to analyze large volumes of complex biological data, thereby greatly accelerating the ability to understand, predict, and design biological processes for beneficial purposes. In the laboratory, the bridging of AI-enabled automated experimental technologies, HPC and digital twins will provide potent tools for researchers to explore the fundamental nature of biology and harness its inherent metabolic potential for a variety of beneficial purposes. The focus of this workshop was on how high-performance computational methods can impact this objective by exploring digital twins, foundational models, and data-driven approaches with applications to advance automated laboratory experiments, modeling of complex living systems and engineering new functions into plants and microbial systems relevant to DOE mission. Workshop attendees with expertise in plant science, microbiology, mathematics, computer science, and AI assessed the current state of the science, trends, and AI challenges at the interface of plant and microbial systems biology and computational science to identify opportunities for high-impact research. This collaborative effort capitalized on ASCR's advancements in applied mathematics, computer science, and Exascale systems, and BER's expertise in basic genomics-enabled research on DOE relevant plant and microbial systems. The workshop culminated in four key priority research directions to guide future research and development within DOE Office of Science programs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale. In medicine, this unlocking of the rich, expressive data within clinical free text in electronic medical records will help untap the full potential of big data for research and clinical purposes. Recent major NLP algorithmic advances have significantly improved the performance of these algorithms, leading to a surge in academic and industry interest in developing tools to automate information extraction and phenotyping from clinical texts. Thus, these technologies are poised to transform medical research and alter clinical practices in the future. Radiation oncology stands to benefit from NLP algorithms if they are appropriately developed and deployed, as they may enable advances such as automated inclusion of radiation therapy details into cancer registries, discovery of novel insights about cancer care, and improved patient data curation and presentation at the point of care. However, challenges remain before the full value of NLP is realized, such as the plethora of jargon specific to radiation oncology, nonstandard nomenclature, a lack of publicly available labeled data for model development, and interoperability limitations between radiation oncology data silos. Successful development and implementation of high quality and high value NLP models for radiation oncology will require close collaboration between computer scientists and the radiation oncology community. Here, we present a primer on artificial intelligence algorithms in general and NLP algorithms in particular; provide guidance on how to assess the performance of such algorithms; review prior research on NLP algorithms for oncology; and describe future avenues for NLP in radiation oncology research and clinics.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

DOE BSSD Performance Management Metrics Report Q1

Microbes play key roles in our biosphere, from driving global nutrient cycling to impacting plant, animal and human health and disease. Complex data from microbial genomes, proteins, and metabolites provide a window into these tiny engines that drive life on our planet. Yet these data are dispersed among researchers’ laboratories and various repositories, making it difficult to access. This calls for new ways of managing data, improving data interoperability, advancing community standards, and creating an infrastructure where data are shared efficiently. We have built the National Microbiome Data Collaborative (NMDC) to advance how scientists create, use, and reuse data to redefine the way we understand and harness the power of microbes. The vision of the National Microbiome Data Collaborative (NMDC) is to drive a microbiome data sharing network connecting data, people, and ideas to advance microbiome innovation and discovery. The NMDC was launched in 2019 and brought together DOE National Laboratories to collaborate across resources, capabilities, and expertise. The NMDC team was strategically assembled to include software developers, microbial researchers, metadata experts, and multi-omics specialists. The diversity of the NMDC team reflects the inherently interdisciplinary nature of microbiome science, and we leverage the strengths of the DOE National Laboratory system. Towards BER’s goal of advancing an iterative systems biology approach to the understanding of microbial genomes, the NMDC serves as a foundation for infrastructure, data standards, and community building. Together with the flagship DOE User Facilities, the Joint Genome Institute (JGI) and the Environmental Molecular Sciences Laboratory (EMSL), we are developing core capabilities in metadata standards for environmental descriptors and sample handling and processing; standardized bioinformatic workflows; an interface for data search and access; and robust community engagement activities. The NMDC production platform supports long-term data infrastructure and community building for BER’s bioenergy and environmental research goals. Our approach leverages lessons learned and an ambitious framework for collaborative, interdisciplinary data infrastructure to support microbiome research. The NMDC supports data, information, and knowledge access through three defined software tools – the Submission Portal, NMDC EDGE, and the Data Portal – driven by community needs. Herein, we describe the value proposition for the microbiome research community, our overarching strategy, and challenges and opportunities for developing the NMDC as both an infrastructure and community engagement program.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

UCB-GLOBES: An open-access mass spectral database of identified and unidentified atmospheric organic compounds

Chemical characterization of atmospheric organic aerosols using gas chromatography with 70 eV electron ionization mass spectrometry (GC/EI-MS) has been used for decades in advancing molecular marker detection and identification, though primarily through suspect screening and/or targeted analyses. To advance non-targeted analyses of environmental samples, we have catalogued approximately 27 000 mass spectra (MS) of the trimethylsilyl derivatives of semi-volatile organic aerosol (OA) analytes in the open-access University of California Berkeley Goldstein Library of Organic Biogenic Environmental Spectra (UCB-GLOBES). Analytes were observed in ambient samples from the U.S. and the Central Amazon and/or laboratory simulations of secondary OA (SOA) formation. These samples are representative of OA under urban and biomass burning influences as well as SOA derived from biogenic precursors (e.g., isoprene, monoterpenes, sesquiterpenes) and biomass burning intermediates. MS are documented in UCB-GLOBES without regard to known chemical identity, annotated with extensive metadata such as sample source/experimental conditions, any structural information gained from MS analyses, and predicted chemical properties such as average carbon oxidation state and carbon number. UCB-GLOBES MS are compatible for importing into the NIST MS Search program, and we have also provided a Jupyter Notebook for MS visualization and comparisons. We demonstrate the utility of UCB-GLOBES through MS reanalyses of prior analytes observed in ambient data, finding a 20 % reduction in the number of analytes assigned to OA source categories reliant solely on time series correlation and an overall 11 % increase in new MS-based OA source categorization for the Southeast U.S. For 1513 analytes observed previously in the Central Amazon, we found 375 MS matches using UCB-GLOBES vs. 136 MS matches during prior analyses, representing a 14 % gain in newly confirmed or newly categorized OA species. While OA from laboratory oxidation experiments in UCB-GLOBES are highly diverse chemically, on average only 29 % of UCB-GLOBES MS have a mass spectral match to another MS entry in UCB-GLOBES and/or in databases of known compounds (i.e. NIST MS Database, Adams Essential Oil, MANE Flavor and Fragrance Company). This indicates that roughly 70 % of UCB-GLOBES MS are unique thus far, not observed more than once among the laboratory oxidation samples and ambient data in UCB-GLOBES MS. Further, only 18 % can be positively identified using these databases or known authentic standards. This points to a large gap between these laboratory simulations and ambient OA. Overall, the UCB-GLOBES database can be utilized for improving confidence in OA source categorization and/or identification, novel chemical marker discovery, tracking chemical diversity, de novo structure and properties prediction, and improving MS search and matching algorithms. This can ultimately inform future research priorities for the chemical characterization of atmospheric organic samples.

Mass spectrometry↗
DOE OSTI2024

Survey of gravitationally lensed objects in HSC imaging (SuGOHI) – X. Strong lens finding in the HSC-SSP using convolutional neural networks

ABSTRACT We apply a novel model based on convolutional neural networks (CNN) to identify gravitationally lensed galaxies in multiband imaging of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) Survey. The trained model is applied to a parent sample of 2350 061 galaxies selected from the $\sim$ 800 deg$^2$ Wide area of the HSC-SSP Public Data Release 2. The galaxies in HSC Wide are selected based on stringent pre-selection criteria, such as multiband magnitudes, stellar mass, star formation rate, extendedness limit, photometric redshift range, etc. The trained CNN assigns a score from 0 to 1, with 1 representing lenses and 0 representing non-lenses. Initially, the CNN selects a total of 20 241 cutouts with a score greater than 0.9, but this number is subsequently reduced to 1522 cutouts after removing definite non-lenses for further visual inspection. We discover 43 grade A (definite) and 269 grade B (probable) strong lens candidates, of which 97 are completely new. In addition, we also discover 880 grade C (possible) lens candidates, 289 of which are known systems in the literature. We identify 143 candidates from the known systems of grade C that had higher confidence in previous searches. Our model can also recover 285 candidate galaxy-scale lenses from the Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI), where a single foreground galaxy acts as the deflector. Even though group-scale and cluster-scale lens systems are not included in the training, a sample of 32 SuGOHI-c (i.e. group/cluster-scale systems) lens candidates is retrieved. Our discoveries will be useful for ongoing and planned spectroscopic surveys, such as the Subaru Prime Focus Spectrograph project, to measure lens and source redshifts in order to enable detailed lens modelling.

Jaelani, Anton T. (ORCID:0000000162825778)↗
DOE OSTI2023

Oak Ridge National Laboratory Annual Sustainability Report 2023

ORNL, managed under contract by UT-Battelle LLC, is DOE’s largest science and energy laboratory and, as such, executes the widest range of mission capabilities. Diverse expertise spans a broad range of scientific and engineering disciplines, enabling research and science achievements to accelerate the delivery of solutions to the marketplace. ORNL supports DOE’s national missions of scientific discovery, clean energy, and security. To execute these activities, ORNL has grown significantly over 80 years of continuous operations, consisting of facilities with commissioning dates ranging from the 1940s to the present—an extraordinary set of distinctive scientific facilities and equipment. The complexities of such a variety of facilities require teamwork among divisions, a wide variety of conservation projects, and creative strategies to achieve the desired energy and water savings. Such a diverse and unique set of major facilities, totaling over 5.5 million square feet, with 6,000 employees, requires an innovative plan to accomplish advancements in operational efficiencies. ORNL is tasked with the management of an extraordinary set of distinctive scientific facilities and equipment for DOE. ORNL is mission-driven, and its mission has grown substantially over the decades. ORNL’s core research capabilities provide broad science and technology support for DOE in the areas of energy, environment, and national security. Currently, ORNL is a world leader in materials, neutron, and nuclear science and engineering, and in high-performance computing and data analytics. ORNL’s vast portfolio of research facilities must be maintained and carefully upgraded to protect the nation’s investment in scientific analysis. The goal of sustainable and resilient operations is to enable more effective execution of ORNL’s science and technology mission. Sustainable operational practices and enhanced resilience strive for excellent results while remaining diligent in energy conservation, environmental stewardship, asset management, and community engagement. The Sustainable ORNL Program (Sustainable ORNL) Continuous improvements in operational and business processes must be integrated into the fabric of the ORNL culture to maximize the return from the investment made in modernizing facilities and equipment. The Sustainable ORNL program promotes the legacy of system-wide best practices, management commitment, and employee engagement that will lead ORNL into a future of efficient, resilient, and sustainable operations. ORNL leadership and Sustainable ORNL champions receive regular status reports on the progress of each project and focus area (i.e., roadmap) and periodic summary reports. More information can be found at the program’s website. The Sustainable ORNL roadmap structure endorses 15 vital roadmaps. The figure below summarizes the current project assignments and demonstrates that each project contributes to the wellbeing of the whole. Continuous employee engagement and regular status reports confirm the ideals of the program. The roadmap structure is not static; as the science mission advances and the needs of the organization evolve, the Sustainable ORNL roadmap structure elements are modified to align with developing priorities. In 2022, Sustainable ORNL made roadmap changes to better align ORNL to support new federal requirements that have been issued.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

DOE Repository Metadata Profile (DRMP): A Metadata Framework for Advancing Interoperability and AI Readiness Across Scientific Repositories

The Department of Energy (DOE) funds a diverse and distributed ecosystem of repositories that steward scientific data, publications, and software across its research programs, user facilities, and national laboratories. While significant progress has been made in standardizing dataset-level metadata, the metadata describing repositories themselves (their identity, governance, access interfaces, policies, and technical capabilities) remains inconsistent and fragmented across DOE-funded systems. This variability limits discoverability, interoperability, automated validation, and AI-driven analysis, all of which are increasingly essential for modern scientific workflows. To address this gap, the DOE Data Curation Working Group (DCWG) developed the DOE Repository Metadata Profile (DRMP). The DRMP is a practical, community-driven framework that defines how repositories can describe themselves in a consistent, machine-actionable, and scalable manner. The DRMP is not a new metadata schema. Instead, it is a mapping profile and structured element set capturing the essential characteristics of DOE repositories. It harmonizes repository-level metadata across six widely adopted community schemas: RE3Data; DCAT-US v3; Schema.org; Dublin Core; DataCite 4.6; and PREMIS 3.0. This harmonization eliminates reinvention and enables interoperability within DOE and across the broader scientific ecosystem. A core objective of the DRMP is to reduce burden on repositories by allowing them to reuse their existing metadata through a Rosetta-style crosswalk rather than redesigning local implementations. The profile introduces a three-level conformance model that supports incremental adoption: • Level 1 – Minimum Viable Record (MVR): foundational identification elements required for workflows, project registration, and basic repository presence. • Level 2 – Interoperable: structured metadata enabling alignment with national and international discovery systems. • Level 3 – AI-Ready: enhanced provenance, policy transparency, fixity, semantic context, and capabilities that support automated reasoning, model training governance, and machine-assisted curation. To support implementation, the DRMP includes JSON Schema definitions, OpenAPI patterns, and MCP templates that allow repositories to publish machine-readable metadata directly within existing platforms. These resources are modular and lightweight, enabling adoption without major architectural change. Adopting the DRMP enables repositories to: • Enhance discoverability and interoperability by aligning identifiers, classifications, and descriptive elements across widely used schema standards. • Support federated discovery and cross-registration across DOE systems, Data.gov, and international catalogs. • Enable AI agents and workflow orchestration systems to interpret repository-level metadata within the American Science Cloud (AmSC) through Model Context Protocol (MCP)-based context publication. • Demonstrate alignment with DOE’s open science, stewardship, and FAIR data priorities. This guidance represents a community-driven step forward. Through voluntary adoption and continued feedback, the DRMP advances a cohesive, machine-actionable description of DOE repositories that supports FAIR data practices, preparing the infrastructure for AI-enabled research, and strengthening the discoverability and reuse of DOE’s scientific outputs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

LDRD FY25 Program Overview

As Lawrence Livermore National Laboratory’s (LLNL’s) Laboratory Directed Research and Development (LDRD) program enters its fifth decade of leading-edge research and development, its impact and importance have never been stronger. The program continues to advance strategic investments in pioneering science, technology, and engineering, ensuring LLNL will be ready to deliver on our mission as it evolves over the coming decades. Investing in LDRD research, and the people who perform this critical work, gives LLNL the ability to sustain our role as a leader in the Department of Energy and National Nuclear Security Administration enterprise. The LDRD program enables high-risk, high-payoff research that anticipates emerging threats and future mission needs. By nurturing the ingenuity of the Lab’s greatest asset, its people, LDRD funding advances not only our research but also grows and nurtures our workforce: engaging future innovators with student mentoring, challenging postdoctoral researchers to apply their skills to support national security, and strengthening the leadership skills of early career staff. This annual report documents how LDRD investments advance LLNL’s science, technology, and engineering across our mission space. To assess LDRD’s impact we track both short and long-term metrics such as peer-reviewed publications, number of students, or professional fellows. In addition to reviewing these metrics, I encourage you to delve deeper into the breadth of science and technology that illustrate the strategic value of this research portfolio. For instance, a recent exploratory research project used advanced manufacturing to construct miniaturized three-dimensional ion traps for a quantum computer with reduced quantum error rates to enable applications that address national security missions and support basic science. Another project has delved into studying detonation by examining deflagration to enhance the safety and security of the nuclear weapons stockpile. LDRD researchers are also deploying AI agents on two of the world’s most powerful supercomputers to automate and accelerate inertial confinement fusion experiments. Other teams are delivering more accurate optical constants to enable improved validation for aluminum to advance atomic and molecular physics models. LDRD-driven discoveries of how metals deform under extreme conditions strengthen our ability to model and design materials for demanding national security environments. National security challenges are increasingly complex and continuously evolving. LDRD focuses our most innovative science and technology on these challenges, ensuring the Laboratory is developing creative, forward-leaning solutions for our nation and the world. The following pages feature highlights of published scientific advances, patents, and honors that stem from LDRD investments. As you read this report, I hope you will understand how these investments position the Laboratory, and our partners, to meet the demands of the decades ahead.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Fundamental Mechanisms of Roughening and Smoothing During Thin Film Deposition

In this research program, we have explored the fundamental limits for thin film deposition in both crystalline and amorphous (i.e. non-crystalline) materials systems. For vacuum-based physical deposition processes such as sputter deposition, the background gas pressure of the inert gas (usually argon) used as the process gas has been found to be a key variable. Both a roughness transition and stress transition as a function of pressure have been linked to a common mechanism involving collisions of energetic particles from the deposition source with the process inert gas. As energetic particles collide with gas molecules in the deposition process they lose their energy rapidly if the pressure (and background gas density) is above a critical value. Both roughness and stress limit important properties of thin films for applications. In the area of epitaxial growth we have also discovered a related effect; there is a critical pressure below which highly crystalline layers grow in a layer-by-layer mode. This effect is also though to be due to energetic particle thermalization and scattering. Several other important effects such as the observation of coalescence dominated growth has been observed. This mode can be likened to the behavior of two-dimensional water droplets on the hood of a car during a rain storm; as the droplets grow and touch each other they tend to coalesce rapidly into new larger circular puddles, and this process proceeds exponentially as larger puddles overtake smaller ones and also merge with other large puddles. This discovery will enable more accurate simulations and modeling of epitaxial growth processes. We have also observed that epitaxial films undergo a roughening transition as a function of thickness, which is attributed to strain induced by the crystalline lattice mismatch with the substrate crystal. In addition, we have studied another physical deposition process called pulsed laser deposition. It differs from sputter deposition due to the pulsed nature of the deposition where particles arrive at the growth surface in an interval of a few microseconds. We have observed effects such as transient formation of two dimensional islands on elemental crystalline surfaces. Pulsed deposition may also lead to non-equilibrium phases in some cases, such as the observation anomalously high tetragonality for ferroelectric thin films. All of the results described above feature in-situ synchrotron X-ray scattering as the main experimental method, which has become an indispensable technique for observing the kinetics of structures forming in real-time. We have also investigated in-situ coherent X-ray scattering and have developed methods to characterize temporal correlations that are not possible to observe with low-coherence X-rays. A high profile result of this work is a new technique to monitor defect propagation velocities in thin films. This has practical significance since defects limit the properties of thin films and it is desirable to understand their properties and origin in order to control them for practical applications. More broadly, amorphous thin films and multilayers have applications in optical devices, including mirrors and filters. Epitaxial thin films and multilayers have applications in electronic devices such as ferroelectric multilayers for non-volatile data storage, and thermoelectric nanostructures for energy conversion. Our progress in this project points the way for improved deposition methods and for improved simulation and modeling of thin film deposition processes for nanoscale control of materials with novel applications in these areas.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Accelerated Testing of Selected Filler Compositions

The fillers R&D program, mostly experimental, is part of a broader R&D program that includes new process modeling and performance assessment of criticality effects and the overall importance of criticality to repository performance (consequence screening). A literature research and consultation effort with experts by Hardin and Brady (2018) identified several potentially effective and workable filler materials including cements (primarily phosphate based), molten-metal alloys, and low-temperature glasses. Filler attributes were defined and the preliminary lists were compared qualitatively. Further comparative analysis will be done (e.g., cost estimates) after experimental screening has narrowed the list of alternatives. The following cement filler compositions were selected for experimental development work and accelerated testing in FY19: Aluminum phosphate cements (APCs); more specifically aluminum oxide / aluminum phosphate (Al 2 O 3 / AlPO 4 ) cements in which Al 2 O 3 serves as the filler material bound by an AlPO 4 binder formed by the reaction of Al 2 O 3 with H 3 PO 4 ; Calcium phosphate cements (CPCs); more specifically composed of pure or nearly pure hydroxyapatite (Ca 5 (PO 4 ) 3 (OH)); Magnesium potassium phosphate cements (MKPs) composed of magnesium oxide / magnesium potassium phosphate (MgO / MgKPO 4 ) cements in which MgO serves as the filler and MgKPO 4 serves as the binder formed by the reaction of MgO with monopotassium phosphate (KH 2 PO 4 ) and tricalcium phosphate ((Ca 3 (PO 4 ) 2 ); Two additional potential cement materials were explored preliminarily as the result of: (1) continued literature investigations into other filler candidates (wollastonite-based phosphate ceramic) and (2) the experimental discovery of a well-consolidated fly ash phosphate cement during the evaluation of fly ash as a potential filler material with Al 2 O 3 in APCs. Fly ash phosphate cements, more specifically in which a fly ash material composed primarily of mullite and quartz serves as the filler and is reacted with H 3 PO 4 to form amorphous phosphate phase(s) as the binder; Wollastonite aluminum phosphate cements (WAPC), specifically wollastonite / aluminum phosphate (CaSiO 3 / AlPO 4 ) in which CaSiO 3 serves as the filler material and AlPO 4 serves as the binder formed by Al(OH) 3 or metakaolin as Al sources and H 3 PO 4 or ammonium dihydrogen phosphate (ADP) (NH 4 H 2 PO 4 ) as phosphate sources. The FY19 effort focused on the optimization of compositions and subsequent processing of these five materials to achieve dense and well-consolidated monolithic samples with relatively low porosity. Once these goals were met basic material properties screening evaluations were performed including an assessment of dissolution resistance in water at elevated temperature (200 °C) and mechanical testing including unconfined compressive strength (UCS) testing. To date, the aluminum phosphate cements (APCs) appear to show the most promise for continued development. They are easily prepared and form smooth pourable slurries that remain stable for days with relatively low viscosities of several thousand centipoise (cP). They are then set at elevated temperatures (e.g., 170 °C) under ambient (0.1 MPa) or elevated pressure (~1MPa). Overall, they demonstrate the best dissolution resistance in water at elevated temperature (200 °C) and good compressive strengths. However, additional effort is required to optimize the APC slurry formulations and the process used for thermal curing these materials. The calcium phosphate cements (CPCs) can be formed at room temperature to produce a well-consolidated body. However, their slurry viscosities are very high (and difficult to measure) and they exhibit relatively short cure times of 2 to 3 hours. Also, dissolution resistance is very poor, the poorest of all the cements examined The same is the case for the small number of MKP cements fabricated; they cure very quickly (10 minutes or less) and disintegrate within a few hours upon immersion in distilled water. Surprisingly, fly ash reacts with phosphoric acid to form dense and well-consolidated cements but the mixture rapidly sets at room temperature (less than 30 minutes) and the subsequent conversion of the binder to an amorphous phosphate phase(s) as a function of temperature is complicated. Finally, the wollastonite aluminum phosphate cements (WAPC) are easily prepared and form smooth pourable slurries that remain stable for several hours. They are then set at 130 °C. A WAPC sample exhibited the highest compressive strengths of all the materials we evaluated but in general their dissolution resistance to water is poor.

36 MATERIALS SCIENCE↗